Overview

Dataset statistics

Number of variables12
Number of observations2035
Missing cells294
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory190.9 KiB
Average record size in memory96.1 B

Variable types

Numeric9
Categorical3

Warnings

Country name has a high cardinality: 149 distinct values High cardinality
Ladder score is highly correlated with Log GDP per capita and 3 other fieldsHigh correlation
Log GDP per capita is highly correlated with Ladder score and 2 other fieldsHigh correlation
Healthy life expectancy is highly correlated with Ladder score and 2 other fieldsHigh correlation
Social support is highly correlated with Ladder score and 2 other fieldsHigh correlation
Freedom to make life choices is highly correlated with Ladder scoreHigh correlation
Ladder score is highly correlated with Log GDP per capita and 3 other fieldsHigh correlation
Log GDP per capita is highly correlated with Ladder score and 2 other fieldsHigh correlation
Healthy life expectancy is highly correlated with Ladder score and 2 other fieldsHigh correlation
Social support is highly correlated with Ladder score and 2 other fieldsHigh correlation
Freedom to make life choices is highly correlated with Ladder scoreHigh correlation
Ladder score is highly correlated with Log GDP per capita and 2 other fieldsHigh correlation
Log GDP per capita is highly correlated with Ladder score and 2 other fieldsHigh correlation
Healthy life expectancy is highly correlated with Ladder score and 1 other fieldsHigh correlation
Social support is highly correlated with Ladder score and 1 other fieldsHigh correlation
Ladder score is highly correlated with Healthy life expectancy and 5 other fieldsHigh correlation
Year is highly correlated with df_index and 1 other fieldsHigh correlation
df_index is highly correlated with Year and 3 other fieldsHigh correlation
Healthy life expectancy is highly correlated with Ladder score and 5 other fieldsHigh correlation
Freedom to make life choices is highly correlated with Ladder score and 5 other fieldsHigh correlation
Generosity is highly correlated with Log GDP per capita and 1 other fieldsHigh correlation
Log GDP per capita is highly correlated with Ladder score and 7 other fieldsHigh correlation
COVID time is highly correlated with Year and 1 other fieldsHigh correlation
Regional indicator is highly correlated with Ladder score and 7 other fieldsHigh correlation
Social support is highly correlated with Ladder score and 4 other fieldsHigh correlation
Perceptions of corruption is highly correlated with Ladder score and 4 other fieldsHigh correlation
Log GDP per capita has 24 (1.2%) missing values Missing
Healthy life expectancy has 51 (2.5%) missing values Missing
Freedom to make life choices has 30 (1.5%) missing values Missing
Generosity has 76 (3.7%) missing values Missing
Perceptions of corruption has 104 (5.1%) missing values Missing
df_index is uniformly distributed Uniform
Country name is uniformly distributed Uniform
df_index has unique values Unique

Reproduction

Analysis started2021-07-11 01:04:20.317895
Analysis finished2021-07-11 01:05:11.426300
Duration51.11 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct2035
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1017
Minimum0
Maximum2034
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2021-07-10T21:05:11.865989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile101.7
Q1508.5
median1017
Q31525.5
95-th percentile1932.3
Maximum2034
Range2034
Interquartile range (IQR)1017

Descriptive statistics

Standard deviation587.5982187
Coefficient of variation (CV)0.5777760263
Kurtosis-1.2
Mean1017
Median Absolute Deviation (MAD)509
Skewness0
Sum2069595
Variance345271.6667
MonotonicityNot monotonic
2021-07-10T21:05:12.337470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
6691
 
< 0.1%
6951
 
< 0.1%
6931
 
< 0.1%
6911
 
< 0.1%
6891
 
< 0.1%
6871
 
< 0.1%
6851
 
< 0.1%
6831
 
< 0.1%
6811
 
< 0.1%
Other values (2025)2025
99.5%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
20341
< 0.1%
20331
< 0.1%
20321
< 0.1%
20311
< 0.1%
20301
< 0.1%
20291
< 0.1%
20281
< 0.1%
20271
< 0.1%
20261
< 0.1%
20251
< 0.1%

Country name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct149
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size16.0 KiB
Ghana
 
16
Zimbabwe
 
16
Japan
 
16
El Salvador
 
16
Colombia
 
16
Other values (144)
1955 

Length

Max length25
Median length7
Mean length8.274692875
Min length4

Characters and Unicode

Total characters16839
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Ghana16
 
0.8%
Zimbabwe16
 
0.8%
Japan16
 
0.8%
El Salvador16
 
0.8%
Colombia16
 
0.8%
Israel16
 
0.8%
Uruguay16
 
0.8%
Kazakhstan16
 
0.8%
Mexico16
 
0.8%
Kyrgyzstan16
 
0.8%
Other values (139)1875
92.1%

Length

2021-07-10T21:05:13.766257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united46
 
1.9%
china42
 
1.7%
south32
 
1.3%
republic29
 
1.2%
of26
 
1.1%
cyprus22
 
0.9%
north22
 
0.9%
cambodia16
 
0.7%
argentina16
 
0.7%
cameroon16
 
0.7%
Other values (159)2168
89.0%

Most occurring characters

ValueCountFrequency (%)
a2629
15.6%
i1484
 
8.8%
n1363
 
8.1%
e1145
 
6.8%
r984
 
5.8%
o933
 
5.5%
t610
 
3.6%
l608
 
3.6%
u521
 
3.1%
s518
 
3.1%
Other values (42)6044
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13962
82.9%
Uppercase Letter2419
 
14.4%
Space Separator400
 
2.4%
Other Punctuation36
 
0.2%
Open Punctuation11
 
0.1%
Close Punctuation11
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2629
18.8%
i1484
10.6%
n1363
9.8%
e1145
 
8.2%
r984
 
7.0%
o933
 
6.7%
t610
 
4.4%
l608
 
4.4%
u521
 
3.7%
s518
 
3.7%
Other values (16)3167
22.7%
Uppercase Letter
ValueCountFrequency (%)
S236
 
9.8%
C229
 
9.5%
M193
 
8.0%
B174
 
7.2%
A169
 
7.0%
P133
 
5.5%
N129
 
5.3%
T127
 
5.3%
I124
 
5.1%
K119
 
4.9%
Other values (12)786
32.5%
Space Separator
ValueCountFrequency (%)
400
100.0%
Open Punctuation
ValueCountFrequency (%)
(11
100.0%
Close Punctuation
ValueCountFrequency (%)
)11
100.0%
Other Punctuation
ValueCountFrequency (%)
.36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16381
97.3%
Common458
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2629
16.0%
i1484
 
9.1%
n1363
 
8.3%
e1145
 
7.0%
r984
 
6.0%
o933
 
5.7%
t610
 
3.7%
l608
 
3.7%
u521
 
3.2%
s518
 
3.2%
Other values (38)5586
34.1%
Common
ValueCountFrequency (%)
400
87.3%
.36
 
7.9%
(11
 
2.4%
)11
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII16839
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2629
15.6%
i1484
 
8.8%
n1363
 
8.1%
e1145
 
6.8%
r984
 
5.8%
o933
 
5.5%
t610
 
3.6%
l608
 
3.6%
u521
 
3.1%
s518
 
3.1%
Other values (42)6044
35.9%

Regional indicator
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.0 KiB
Sub-Saharan Africa
426 
Latin America and Caribbean
299 
Western Europe
292 
Central and Eastern Europe
242 
Middle East and North Africa
228 
Other values (5)
548 

Length

Max length34
Median length18
Mean length21.34987715
Min length9

Characters and Unicode

Total characters43447
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Asia
2nd rowSouth Asia
3rd rowSouth Asia
4th rowSouth Asia
5th rowSouth Asia

Common Values

ValueCountFrequency (%)
Sub-Saharan Africa426
20.9%
Latin America and Caribbean299
14.7%
Western Europe292
14.3%
Central and Eastern Europe242
11.9%
Middle East and North Africa228
11.2%
Commonwealth of Independent States182
8.9%
Southeast Asia125
 
6.1%
South Asia91
 
4.5%
East Asia88
 
4.3%
North America and ANZ62
 
3.0%

Length

2021-07-10T21:05:14.702205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-10T21:05:14.900899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
and831
13.1%
africa654
 
10.3%
europe534
 
8.4%
sub-saharan426
 
6.7%
america361
 
5.7%
east316
 
5.0%
asia304
 
4.8%
caribbean299
 
4.7%
latin299
 
4.7%
western292
 
4.6%
Other values (11)2008
31.8%

Most occurring characters

ValueCountFrequency (%)
a5614
12.9%
4289
 
9.9%
e3525
 
8.1%
n3359
 
7.7%
r3340
 
7.7%
t2750
 
6.3%
i2145
 
4.9%
d1651
 
3.8%
o1586
 
3.7%
s1461
 
3.4%
Other values (20)13727
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32871
75.7%
Uppercase Letter5861
 
13.5%
Space Separator4289
 
9.9%
Dash Punctuation426
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a5614
17.1%
e3525
10.7%
n3359
10.2%
r3340
10.2%
t2750
8.4%
i2145
 
6.5%
d1651
 
5.0%
o1586
 
4.8%
s1461
 
4.4%
u1176
 
3.6%
Other values (8)6264
19.1%
Uppercase Letter
ValueCountFrequency (%)
A1381
23.6%
S1250
21.3%
E1092
18.6%
C723
12.3%
N352
 
6.0%
L299
 
5.1%
W292
 
5.0%
M228
 
3.9%
I182
 
3.1%
Z62
 
1.1%
Space Separator
ValueCountFrequency (%)
4289
100.0%
Dash Punctuation
ValueCountFrequency (%)
-426
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin38732
89.1%
Common4715
 
10.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a5614
14.5%
e3525
 
9.1%
n3359
 
8.7%
r3340
 
8.6%
t2750
 
7.1%
i2145
 
5.5%
d1651
 
4.3%
o1586
 
4.1%
s1461
 
3.8%
A1381
 
3.6%
Other values (18)11920
30.8%
Common
ValueCountFrequency (%)
4289
91.0%
-426
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII43447
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a5614
12.9%
4289
 
9.9%
e3525
 
8.1%
n3359
 
7.7%
r3340
 
7.7%
t2750
 
6.3%
i2145
 
4.9%
d1651
 
3.8%
o1586
 
3.7%
s1461
 
3.4%
Other values (20)13727
31.6%

Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct17
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.826536
Minimum2005
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2021-07-10T21:05:15.242251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2006
Q12010
median2014
Q32018
95-th percentile2021
Maximum2021
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.514249649
Coefficient of variation (CV)0.002241627851
Kurtosis-1.081594923
Mean2013.826536
Median Absolute Deviation (MAD)4
Skewness-0.1168037387
Sum4098137
Variance20.37844989
MonotonicityNot monotonic
2021-07-10T21:05:15.685197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2021149
 
7.3%
2019144
 
7.1%
2017143
 
7.0%
2018142
 
7.0%
2014138
 
6.8%
2016138
 
6.8%
2015137
 
6.7%
2011136
 
6.7%
2012135
 
6.6%
2013132
 
6.5%
Other values (7)641
31.5%
ValueCountFrequency (%)
200527
 
1.3%
200687
4.3%
200799
4.9%
2008107
5.3%
2009108
5.3%
2010118
5.8%
2011136
6.7%
2012135
6.6%
2013132
6.5%
2014138
6.8%
ValueCountFrequency (%)
2021149
7.3%
202095
4.7%
2019144
7.1%
2018142
7.0%
2017143
7.0%
2016138
6.8%
2015137
6.7%
2014138
6.8%
2013132
6.5%
2012135
6.6%

Ladder score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1601
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.490948403
Minimum2.375
Maximum8.019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2021-07-10T21:05:16.529082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.375
5-th percentile3.7142
Q14.669
median5.42
Q36.298
95-th percentile7.3871
Maximum8.019
Range5.644
Interquartile range (IQR)1.629

Descriptive statistics

Standard deviation1.107522756
Coefficient of variation (CV)0.2016997202
Kurtosis-0.6845532752
Mean5.490948403
Median Absolute Deviation (MAD)0.803
Skewness0.06105199064
Sum11174.08
Variance1.226606656
MonotonicityNot monotonic
2021-07-10T21:05:16.830489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.2525
 
0.2%
5.7864
 
0.2%
6.3094
 
0.2%
5.844
 
0.2%
5.0744
 
0.2%
6.694
 
0.2%
4.7414
 
0.2%
5.3044
 
0.2%
6.5614
 
0.2%
4.5744
 
0.2%
Other values (1591)1994
98.0%
ValueCountFrequency (%)
2.3751
< 0.1%
2.5231
< 0.1%
2.6621
< 0.1%
2.6942
0.1%
2.7021
< 0.1%
2.8081
< 0.1%
2.8391
< 0.1%
2.9031
< 0.1%
2.9051
< 0.1%
2.9361
< 0.1%
ValueCountFrequency (%)
8.0191
< 0.1%
7.9711
< 0.1%
7.8891
< 0.1%
7.8581
< 0.1%
7.8421
< 0.1%
7.8341
< 0.1%
7.7882
0.1%
7.781
< 0.1%
7.7761
< 0.1%
7.7711
< 0.1%

Log GDP per capita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1544
Distinct (%)76.8%
Missing24
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean9.391096469
Minimum6.635
Maximum11.648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2021-07-10T21:05:17.125552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6.635
5-th percentile7.405
Q18.484
median9.487
Q310.3705
95-th percentile10.9285
Maximum11.648
Range5.013
Interquartile range (IQR)1.8865

Descriptive statistics

Standard deviation1.141128578
Coefficient of variation (CV)0.1215117512
Kurtosis-0.8678665426
Mean9.391096469
Median Absolute Deviation (MAD)0.946
Skewness-0.3220532555
Sum18885.495
Variance1.302174431
MonotonicityNot monotonic
2021-07-10T21:05:17.513883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.5465
 
0.2%
9.6074
 
0.2%
10.8614
 
0.2%
9.2624
 
0.2%
7.9264
 
0.2%
9.5694
 
0.2%
9.1864
 
0.2%
9.7824
 
0.2%
9.244
 
0.2%
10.1664
 
0.2%
Other values (1534)1970
96.8%
(Missing)24
 
1.2%
ValueCountFrequency (%)
6.6352
0.1%
6.6781
< 0.1%
6.7191
< 0.1%
6.7231
< 0.1%
6.7421
< 0.1%
6.7481
< 0.1%
6.7761
< 0.1%
6.7871
< 0.1%
6.8231
< 0.1%
6.8381
< 0.1%
ValueCountFrequency (%)
11.6481
< 0.1%
11.6471
< 0.1%
11.6451
< 0.1%
11.641
< 0.1%
11.6341
< 0.1%
11.6171
< 0.1%
11.5981
< 0.1%
11.5951
< 0.1%
11.5921
< 0.1%
11.581
< 0.1%

Healthy life expectancy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct890
Distinct (%)44.9%
Missing51
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean63.6952122
Minimum32.3
Maximum77.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2021-07-10T21:05:17.829676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum32.3
5-th percentile49.266
Q159.18
median65.4
Q368.8
95-th percentile73.1
Maximum77.1
Range44.8
Interquartile range (IQR)9.62

Descriptive statistics

Standard deviation7.376080279
Coefficient of variation (CV)0.1158027429
Kurtosis0.04337824949
Mean63.6952122
Median Absolute Deviation (MAD)4.76
Skewness-0.7703262842
Sum126371.301
Variance54.40656028
MonotonicityNot monotonic
2021-07-10T21:05:18.218615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.216
 
0.8%
7315
 
0.7%
66.414
 
0.7%
72.414
 
0.7%
72.614
 
0.7%
67.214
 
0.7%
66.613
 
0.6%
65.212
 
0.6%
66.312
 
0.6%
66.812
 
0.6%
Other values (880)1848
90.8%
(Missing)51
 
2.5%
ValueCountFrequency (%)
32.31
< 0.1%
36.861
< 0.1%
40.31
< 0.1%
40.381
< 0.1%
40.8081
< 0.1%
41.21
< 0.1%
41.421
< 0.1%
41.581
< 0.1%
42.11
< 0.1%
42.861
< 0.1%
ValueCountFrequency (%)
77.11
< 0.1%
76.9531
< 0.1%
76.821
< 0.1%
76.81
< 0.1%
76.51
< 0.1%
76.21
< 0.1%
75.91
< 0.1%
75.681
< 0.1%
75.461
< 0.1%
75.21
< 0.1%

Social support
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct450
Distinct (%)22.2%
Missing9
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean0.814958539
Minimum0.291
Maximum0.987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2021-07-10T21:05:18.761999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.291
5-th percentile0.577
Q10.751
median0.836
Q30.90675
95-th percentile0.951
Maximum0.987
Range0.696
Interquartile range (IQR)0.15575

Descriptive statistics

Standard deviation0.1161247148
Coefficient of variation (CV)0.1424915615
Kurtosis1.007588025
Mean0.814958539
Median Absolute Deviation (MAD)0.075
Skewness-1.068720604
Sum1651.106
Variance0.01348494939
MonotonicityNot monotonic
2021-07-10T21:05:19.085953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.81815
 
0.7%
0.93715
 
0.7%
0.87814
 
0.7%
0.9114
 
0.7%
0.95414
 
0.7%
0.91714
 
0.7%
0.83214
 
0.7%
0.90914
 
0.7%
0.93113
 
0.6%
0.82713
 
0.6%
Other values (440)1886
92.7%
ValueCountFrequency (%)
0.2912
0.1%
0.3031
< 0.1%
0.3261
< 0.1%
0.3731
< 0.1%
0.3821
< 0.1%
0.421
< 0.1%
0.4221
< 0.1%
0.4341
< 0.1%
0.4351
< 0.1%
0.4361
< 0.1%
ValueCountFrequency (%)
0.9871
 
< 0.1%
0.9851
 
< 0.1%
0.9841
 
< 0.1%
0.9834
0.2%
0.9822
0.1%
0.981
 
< 0.1%
0.9792
0.1%
0.9772
0.1%
0.9761
 
< 0.1%
0.9751
 
< 0.1%

Freedom to make life choices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct533
Distinct (%)26.6%
Missing30
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean0.7482693267
Minimum0.258
Maximum0.985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2021-07-10T21:05:19.427014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.258
5-th percentile0.487
Q10.656
median0.769
Q30.861
95-th percentile0.936
Maximum0.985
Range0.727
Interquartile range (IQR)0.205

Descriptive statistics

Standard deviation0.1392889173
Coefficient of variation (CV)0.1861481051
Kurtosis-0.05130860423
Mean0.7482693267
Median Absolute Deviation (MAD)0.102
Skewness-0.6477172894
Sum1500.28
Variance0.01940140248
MonotonicityNot monotonic
2021-07-10T21:05:19.702909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.88211
 
0.5%
0.74910
 
0.5%
0.81710
 
0.5%
0.89110
 
0.5%
0.90410
 
0.5%
0.78310
 
0.5%
0.93510
 
0.5%
0.82410
 
0.5%
0.87710
 
0.5%
0.8389
 
0.4%
Other values (523)1905
93.6%
(Missing)30
 
1.5%
ValueCountFrequency (%)
0.2581
< 0.1%
0.261
< 0.1%
0.2871
< 0.1%
0.2951
< 0.1%
0.3041
< 0.1%
0.3061
< 0.1%
0.3151
< 0.1%
0.3321
< 0.1%
0.3331
< 0.1%
0.3351
< 0.1%
ValueCountFrequency (%)
0.9851
 
< 0.1%
0.9841
 
< 0.1%
0.981
 
< 0.1%
0.9711
 
< 0.1%
0.974
0.2%
0.9691
 
< 0.1%
0.9651
 
< 0.1%
0.9642
0.1%
0.9632
0.1%
0.9624
0.2%

Generosity
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct613
Distinct (%)31.3%
Missing76
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean-0.002345584482
Minimum-0.335
Maximum0.698
Zeros3
Zeros (%)0.1%
Negative1120
Negative (%)55.0%
Memory size16.0 KiB
2021-07-10T21:05:20.053455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.335
5-th percentile-0.228
Q1-0.117
median-0.029
Q30.089
95-th percentile0.3022
Maximum0.698
Range1.033
Interquartile range (IQR)0.206

Descriptive statistics

Standard deviation0.1622571652
Coefficient of variation (CV)-69.17557925
Kurtosis0.892332942
Mean-0.002345584482
Median Absolute Deviation (MAD)0.102
Skewness0.834767301
Sum-4.595
Variance0.02632738766
MonotonicityNot monotonic
2021-07-10T21:05:20.348683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.05513
 
0.6%
-0.01610
 
0.5%
-0.10410
 
0.5%
-0.0310
 
0.5%
0.01810
 
0.5%
-0.1019
 
0.4%
-0.0719
 
0.4%
-0.1479
 
0.4%
-0.079
 
0.4%
-0.0099
 
0.4%
Other values (603)1861
91.4%
(Missing)76
 
3.7%
ValueCountFrequency (%)
-0.3351
< 0.1%
-0.3161
< 0.1%
-0.3071
< 0.1%
-0.3052
0.1%
-0.3032
0.1%
-0.2961
< 0.1%
-0.2951
< 0.1%
-0.2932
0.1%
-0.2921
< 0.1%
-0.291
< 0.1%
ValueCountFrequency (%)
0.6981
< 0.1%
0.6891
< 0.1%
0.6881
< 0.1%
0.6791
< 0.1%
0.651
< 0.1%
0.6451
< 0.1%
0.5611
< 0.1%
0.5551
< 0.1%
0.5531
< 0.1%
0.5421
< 0.1%

Perceptions of corruption
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct588
Distinct (%)30.5%
Missing104
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean0.7462770585
Minimum0.035
Maximum0.983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2021-07-10T21:05:20.878276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.035
5-th percentile0.309
Q10.69
median0.801
Q30.87
95-th percentile0.941
Maximum0.983
Range0.948
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.1867596593
Coefficient of variation (CV)0.250255126
Kurtosis1.877472058
Mean0.7462770585
Median Absolute Deviation (MAD)0.085
Skewness-1.507630876
Sum1441.061
Variance0.03487917035
MonotonicityNot monotonic
2021-07-10T21:05:21.398346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.84415
 
0.7%
0.75513
 
0.6%
0.86813
 
0.6%
0.84813
 
0.6%
0.84113
 
0.6%
0.89812
 
0.6%
0.88411
 
0.5%
0.86311
 
0.5%
0.85611
 
0.5%
0.85511
 
0.5%
Other values (578)1808
88.8%
(Missing)104
 
5.1%
ValueCountFrequency (%)
0.0351
< 0.1%
0.0471
< 0.1%
0.061
< 0.1%
0.0641
< 0.1%
0.0661
< 0.1%
0.071
< 0.1%
0.0781
< 0.1%
0.0811
< 0.1%
0.0821
< 0.1%
0.0951
< 0.1%
ValueCountFrequency (%)
0.9832
0.1%
0.9791
 
< 0.1%
0.9772
0.1%
0.9762
0.1%
0.9741
 
< 0.1%
0.9732
0.1%
0.972
0.1%
0.9691
 
< 0.1%
0.9683
0.1%
0.9673
0.1%

COVID time
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.0 KiB
Pre-COVID
1791 
Post-COVID
244 

Length

Max length10
Median length9
Mean length9.11990172
Min length9

Characters and Unicode

Total characters18559
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPre-COVID
2nd rowPre-COVID
3rd rowPre-COVID
4th rowPre-COVID
5th rowPre-COVID

Common Values

ValueCountFrequency (%)
Pre-COVID1791
88.0%
Post-COVID244
 
12.0%

Length

2021-07-10T21:05:22.119811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-10T21:05:22.321682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
pre-covid1791
88.0%
post-covid244
 
12.0%

Most occurring characters

ValueCountFrequency (%)
P2035
11.0%
-2035
11.0%
C2035
11.0%
O2035
11.0%
V2035
11.0%
I2035
11.0%
D2035
11.0%
r1791
9.7%
e1791
9.7%
o244
 
1.3%
Other values (2)488
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter12210
65.8%
Lowercase Letter4314
 
23.2%
Dash Punctuation2035
 
11.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P2035
16.7%
C2035
16.7%
O2035
16.7%
V2035
16.7%
I2035
16.7%
D2035
16.7%
Lowercase Letter
ValueCountFrequency (%)
r1791
41.5%
e1791
41.5%
o244
 
5.7%
s244
 
5.7%
t244
 
5.7%
Dash Punctuation
ValueCountFrequency (%)
-2035
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16524
89.0%
Common2035
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P2035
12.3%
C2035
12.3%
O2035
12.3%
V2035
12.3%
I2035
12.3%
D2035
12.3%
r1791
10.8%
e1791
10.8%
o244
 
1.5%
s244
 
1.5%
Common
ValueCountFrequency (%)
-2035
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII18559
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P2035
11.0%
-2035
11.0%
C2035
11.0%
O2035
11.0%
V2035
11.0%
I2035
11.0%
D2035
11.0%
r1791
9.7%
e1791
9.7%
o244
 
1.3%
Other values (2)488
 
2.6%

Interactions

2021-07-10T21:04:34.761162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:35.237218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:35.667582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:36.046764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:36.312704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:36.583805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:36.960134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:37.251244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:37.580828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:37.984598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:38.362998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:38.838221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:39.192029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:39.460166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:39.710894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:40.202383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:40.469643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:40.734858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:41.007629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:41.288040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:41.551197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:41.798488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:42.053817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:42.292546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:42.707967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:42.978247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:43.261765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:43.544396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:43.901685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:44.542157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:44.909785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:45.263135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:45.546088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:46.113352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:46.462110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:46.841782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:47.168738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:47.707618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:48.010368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:48.310218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:48.657678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:49.018964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:49.490623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:49.924851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:50.154754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:50.381417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:50.680910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:50.964688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:51.510244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:51.933501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:52.244315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:53.062361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:53.569138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:54.059244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:54.486531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:54.938586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:55.350792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:55.780381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:56.235914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:56.585579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:56.997234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:57.423910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:57.792286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:58.161490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:58.458411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:58.826916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:59.089314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:04:59.583519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:05:00.596880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:05:01.879707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:05:02.218096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:05:02.495815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:05:03.436359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:05:04.001777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:05:04.370357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:05:04.780509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:05:05.185847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:05:05.948368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:05:06.801940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:05:07.084707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-10T21:05:07.549737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-07-10T21:05:22.509358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-10T21:05:23.081983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-10T21:05:23.482148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-10T21:05:24.043633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-10T21:05:24.597773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-10T21:05:08.151485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-10T21:05:09.068988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-07-10T21:05:10.030617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-07-10T21:05:10.876709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexCountry nameRegional indicatorYearLadder scoreLog GDP per capitaHealthy life expectancySocial supportFreedom to make life choicesGenerosityPerceptions of corruptionCOVID time
00AfghanistanSouth Asia20083.7247.37050.8000.4510.7180.1680.882Pre-COVID
111AfghanistanSouth Asia20192.3757.69752.4000.4200.394-0.1080.924Pre-COVID
210AfghanistanSouth Asia20182.6947.69252.6000.5080.374-0.0940.928Pre-COVID
39AfghanistanSouth Asia20172.6627.69752.8000.4910.427-0.1210.954Pre-COVID
48AfghanistanSouth Asia20164.2207.69753.0000.5590.5230.0420.793Pre-COVID
57AfghanistanSouth Asia20153.9837.70253.2000.5290.3890.0800.881Pre-COVID
62034AfghanistanSouth Asia20212.5237.69552.4930.4630.382-0.1020.924Post-COVID
75AfghanistanSouth Asia20133.5727.72552.5600.4840.5780.0610.823Pre-COVID
84AfghanistanSouth Asia20123.7837.70552.2400.5210.5310.2360.776Pre-COVID
93AfghanistanSouth Asia20113.8327.62051.9200.5210.4960.1620.731Pre-COVID

Last rows

df_indexCountry nameRegional indicatorYearLadder scoreLog GDP per capitaHealthy life expectancySocial supportFreedom to make life choicesGenerosityPerceptions of corruptionCOVID time
20251878ZimbabweSub-Saharan Africa20134.6907.98550.9600.7990.576-0.1040.831Pre-COVID
20261872ZimbabweSub-Saharan Africa20073.2807.66642.8600.8280.456-0.0820.946Pre-COVID
20271876ZimbabweSub-Saharan Africa20114.8467.84648.1200.8650.633-0.0880.830Pre-COVID
20281875ZimbabweSub-Saharan Africa20104.6827.72946.7000.8570.665-0.0930.828Pre-COVID
20291874ZimbabweSub-Saharan Africa20094.0567.56345.4200.8060.411-0.0780.931Pre-COVID
20301873ZimbabweSub-Saharan Africa20083.1747.46144.1400.8430.344-0.0900.964Pre-COVID
20311871ZimbabweSub-Saharan Africa20063.8267.71141.5800.8220.431-0.0760.905Pre-COVID
20322033ZimbabweSub-Saharan Africa20213.1457.94356.2010.7500.677-0.0470.821Post-COVID
20331877ZimbabweSub-Saharan Africa20124.9557.98349.5400.8960.470-0.1030.859Pre-COVID
20341885ZimbabweSub-Saharan Africa20203.1607.82956.8000.7170.643-0.0090.789Post-COVID